A new algorithm for computing a root of transcendental equations using series expansion

Abstract In this paper, we discuss a new algorithm to find a non-zero real root of the transcendental equations using series expansion. This proposed method is based on the inverse series expansion, which gives a good approximate root than some other existing methods. The implementation of this algorithm is presented in Matlab and Maple. Sample numerical examples are presented to illustrate and validate the efficiency of the proposed algorithm. The method will help to implement in the commercial package for finding a real root of a given transcendental equation.

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Southeast-Asian J. of Sciences, Vol. 7, No. 2 (2019) pp. 106-114 A NEW ALGORITHM FOR COMPUTING A ROOT OF TRANSCENDENTAL EQUATIONS USING SERIES EXPANSION Srinivasarao Thota∗ and Tekle Gemechu Department of Applied Mathematics School of Applied Natural Sciences Adama Science and Technology University Post Box No. 1888, Adama, Ethiopia e-mail: srinithota@ymail.com; tekgem@yahoo.com Abstract In this paper, we discuss a new algorithm to find a non-zero real root of the transcendental equations using series expansion. This proposed method is based on the inverse series expansion, which gives a good ap- proximate root than some other existing methods. The implementation of this algorithm is presented in Matlab and Maple. Sample numerical examples are presented to illustrate and validate the efficiency of the pro- posed algorithm. The method will help to implement in the commercial package for finding a real root of a given transcendental equation. 1 Introduction In science, engineering and computing, solving for root or finding a root of transcendental equations is playing important role. In the literature, we have many well known algorithms for root finding, (for example, Bisection,Muller’s, Regula-Falsi, Secant, Newton-Raphson methods etc.); the modified and hybrid algorithms for finding an approximate root are available in literature, see for example [2, 12, 10, 11, 5, 1, 15, 16, 18, 17, 19, 4, 22, 27, 21, 20, 9, 14, 6, 28, 7, 25, 26]. In the present work, we propose a new method based on series expansion, which finds quick root in comparison with other existing algorithms. The rest ∗Corresponding Author. Email: srinivasarao.thota@astu.edu.et Key words: Root finding algorithms, Transcendental equations, Series expansion, Real root. 2010 AMS Classification: 65Hxx, 65H04. 106 107 of paper is arranged as follows: Section 2 describes the proposed method, their mathematical formulation, calculation steps and flow chart; implementation of the proposed algorithm in Maple and matlab is presented in Section 3 with sample computations; and Section 4 discuss some numerical examples to illus- trate the algorithm and comparisons are made to show efficiency of the new algorithm. 2 Proposed Algorithm The new iterative formula using series expansion is proposed as xn+1 = xn ( xnf ′(xn) f(xn) + xnf ′(xn) ) , n = 0, 1, 2, . . . . (1) By expanding this iterative formula, one can obtain the standard Newton- Raphson method as in first two terms. This is shown in the following theorem. Theorem 1. Suppose α = 0 is a real exact root of f(x) and θ is a sufficiently small neighbourhood of α. Let f ′′(x) exist and f ′(x) = 0 in θ. Then the iterative formula given in equation (1) produces a sequence of iterations {xn : n = 0, 1, 2, . . .} with order of convergence p ≥ 2. Proof. The iterative formula given in equation (1) can be expressed in the following form xn+1 = xn ( xnf ′(xn) f(xn) + xnf ′(xn) ) . Since lim xn→α ( xnf ′(xn) f(xn) + xnf ′(xn) ) = 1, and hence xn+1 = α. This implies that α is a fixed point of the function t(x). Using the standard expansion of (1− x)−1 as 1 1− x = 1 + x + x 2 + x3 + x4 + · · · (2) 108 A New Algorithm for Computing a root of... and from equations (1) and (2), we have xn+1 = xn ( xnf ′(xn) f(xn) + xnf ′(xn) ) = xn ⎛ ⎝ 1 1− ( −f(xn) xnf ′(xn) ) ⎞ ⎠ = xn(1 + ( −f(xn) xnf ′(xn) ) + ( −f(xn) xnf ′(xn) )2 + ( −f(xn) xnf ′(xn) )3 + ( −f(xn) xnf ′(xn) )4 + · · · ) = xn − f(xn) f ′(xn) + 1 xn ( f(xn) f ′(xn) )2 − 1 x2n ( f(xn) f ′(xn) )3 + o ( 1 x3n ( f(xn) f ′(xn) )4) . Since f(xn) ≈ 0, when we neglect higher order terms, then the above equa- tion becomes Newton-Rapson method. Indeed, we have the following formulae obtained from first two, three and four terms of the expansion respectively. xn+1 = xn − f(xn) f ′(xn) . xn+1 = xn − f(xn) f ′(xn) + 1 xn ( f(xn) f ′(xn) )2 . xn+1 = xn − f(xn) f ′(xn) + 1 xn ( f(xn) f ′(xn) )2 − 1 x2n ( f(xn) f ′(xn) )3 . In the above equations, we obtained Newton-Rapson method having quadratic convergence in first two terms. Therefore, the order of convergence of proposed methods (1) is at least p ≥ 2.  2.1 Steps for Computing Root I Select an approximation xn = 0. II Apply the iterative formula given in equation (1). III Repeat Step II until we get desired approximate root, for n = 0, 1, 2, . . .. Flow chat of the proposed algorithm is presented in Figure 1 109 Select Two Approximations of root If approximate root satisfy the desired residual If approximate root satisfies the desired residual then STOP Compute Figure 1: Flow chart for proposed algorithm 3 Implementation of Proposed Algorithm In this section, we discuss the MATLAB and Maple implementation of the proposed algorithm. The data type SeriesNewton(f,x0,esp,n) describes the implementation in MATLAB, where f is given non-linear transcendental func- tion, x0 is the initial approximation of the root, esp is the relative error and n is the number of iterations required. function root = SeriesNewton(f,x0,esp,n) iter = 0; ea = 0; xn = x0; fd = inline(char(diff(formula(f))),’x’); disp(’ ----------------------------’); disp(’ No Root f(Root) %error ’); disp(’ ---------------------------’); while (1) xnold = xn; xn =x0*(x(0)*fd(x0)/(f(x0)+x0*fd(x0))); xnnew = xn; disp(sprintf(’%4d %10.4f %10.2f %8.2f’,iter+1,xn,f(xn),ea)); 110 A New Algorithm for Computing a root of... iter = iter + 1; if xn = 0, ea = abs((xn - xnold)/xn) * 100; end x0 = xn; if ea = n, break, end end disp(’ --------------------------------------’); disp([’Given function f(x) = ’ char(f)]); disp(sprintf(’Approximate root = %10.10f’,xn)); The following a data type SeriesNewton(f,x0,n) gives the implementation in Maple, where f is given non-linear transcendental function, x0 is the initial approximation of the root, and n is the number of iterations required. SeriesNewton:=proc(f,x0,n) local iten, fx0; for iten from 1 by 1 while iten < n+1 do printf("Iteration %g : ", iten); x0:=(x0*(x0*subs(x=x0,diff(f,x))/ (subs(x=x0,f)+x0*subs(x=x0,diff(f,x))))); fx0:=subs(x = x0, f); end do; return x0,fx0; end proc: Sample computations using the implementation of the proposed algorithm are presented in Section 4. 4 Numerical Examples This section provides some numerical examples to discuss the algorithm pre- sented in Section 2 and comparisons are taken into account to conform that the algorithm is more efficient than other existing methods. Example 4.1. Consider the following transcendental equations [11]. We compare the number of iterations required to get approximation root with accu- racy of 10−15. The numerical results are provided in Table 1. a. f(x) = ln(x), with initial approximation 0.5. b. f(x) = x− esin(x) + 1, with initial approximation 1.5. c. f(x) = xe−x − 0.1, with initial approximation 0.1. 111 Table 1: Comparing No. of iterations by different methods Fun. Exact Root Regula Falsi method Newton Raphson method Steffen method Proposed method a. 1.00000 27 Divergent Failure 3 b. 1.69681 & 0 32 Not Convergent Failure 6 c. 0.11183 15 Failure Failure 3 The numerical results given in Table 1 shows that the proposed method is more efficient than other methods. Example 4.2. Consider a transcendental equations of the following type. We find approximate root using formulae different existing methods to show the convergence of the proposed algorithm gives a root faster than other methods, see Table 2. To find approximate root, we start with an initial approximation as 1.5 for Newton-Raphson and Proposed methods; a = 0, b = 1.5 for bisection and regula-falsi methods. We have an exact real root is 0.5. f(x) = 2x3 + 11x2 + 12x− 9. (3) Table 2: Comparing approximate root using proposed algorithm with other existing methods Iteration No. Newton-Raphson method Bisection methods Regula-Falsi method Proposed method 1 0.8076923077 0.75 0.2727273 1.026315789 2 0.5428093643 0.375 0.4044266 0.7296759182 3 0.5010101572 0.5625 0.4612480 0.5699486582 4 0.5001005827 0.46875 0.4845290 0.5097474998 5 0.5000105835 0.515625 0.4938624 0.5002347438 6 0.5000005827 0.4921875 0.4975712 0.5000001415 7 0.5000000009 0.5039063 0.4990399 0.4999999998 Example 4.3. This example gives the sample computation using MatLab and Maple implementation as described in Section 3. Consider a transcendental equation of the form f(x) = 2x3 + 11x2 + 12x− 9 112 A New Algorithm for Computing a root of... with initial approximation of the root as 1.5. Using MatLab implementation, we have the following computations. f=inline(’2*x∧3+11*x∧2+12*x-9’,’x’); function root = SeriesNewton(f,1.5,0.00001,9) ------------------------------------------------------ No Root f(Root) %error ------------------------------------------------------ 1 1.026315789 17.0644 --- 2 0.7296759182 6.38981 10.60666586 3 0.5699486582 1.78293 0.4217779832 4 0.5097474998 0.240146 0.0007051480758 5 0.5002347438 0.005752 3.526445668*10−8 6 0.5000001415 3.467e-06 0 7 0.4999999998 -4e-09 0 8 0.4999999996 -7e-09 0 9 0.4999999998 -4e-09 0 ----------------------------------------------------- Given function f(x) = 2*x∧3+11*x∧2+12*x-9 Approximate root = 0.4999999998 Using Maple implementation, we have the following computations. > f:= 2*x∧3+11*x∧2+12*x-9: > ExpNewton(f,1.5,9); Iteration 1 : 1.026315789 Iteration 2 : 0.7296759182 Iteration 3 : 0.5699486582 Iteration 4 : 0.5097474998 Iteration 5 : 0.5002347438 Iteration 6 : 0.5000001415 Iteration 7 : 0.4999999998 Iteration 8 : 0.4999999996 Iteration 9 : 0.4999999998 One can use the implementation of the proposed algorithm to speed up the manual calculations. 113 5 Conclusion In this present work, we presented a new algorithm to compute an approxi- mate real root of a given (nonlinear) transcendental equation. 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